SCIENCE@home

Integrating Multiple Data Types to Connect Ecological Theory and Data Among Levels

J. Yen,Z. Tonkin,J. Lyon,W. Koster,A. Kitchingman,K. Stamation,P. Vesk

Published 2019 in Frontiers in Ecology and Evolution

ABSTRACT

Ecological theories often encompass multiple levels of biological organisation, such as genes, individuals, populations, and communities. Despite substantial progress towards ecological theory spanning multiple levels, ecological data rarely are connected in this way. This is unfortunate because different types of ecological data often emerge from the same underlying processes and, therefore, are naturally connected among levels. Here, we present an approach to integrate data collected at multiple levels (e.g., individuals, populations) in a single statistical analysis. The resulting integrated models make full use of existing data and align statistical ecology with ecological theories that span multiple levels of organisation. Integrated models are increasingly feasible due to recent advances in computational statistics, which allow fast calculations of multiple likelihoods that depend on complex mechanistic models. We discuss recently developed integrated models and demonstrate their implementation and application using data on freshwater fishes in south-eastern Australia. Available data on freshwater fishes include population survey data, mark-recapture data, and individual growth trajectories. We use these data to estimate demographic vital rates (size-specific survival and reproduction) more accurately than previously possible. We show that integrating multiple data types enables parameter estimates that would otherwise be infeasible and argue that integrated models will strengthen the development of ecological theory in the face of limited data. Although integrated models remain conceptually and computationally challenging, integrating ecological data among levels is likely to be an important step towards unifying ecology among levels.

PUBLICATION RECORD

  • Publication year

    2019

  • Venue

    Frontiers in Ecology and Evolution

  • Publication date

    2019-04-03

  • Fields of study

    Biology, Computer Science, Environmental Science

  • Identifiers
    DOI 10.3389/fevo.2019.00095
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

CITATION MAP

EXTRACTION MAP

CLAIMS

  • No claims are published for this paper.

CONCEPTS

  • No concepts are published for this paper.

REFERENCES

Showing 1-40 of 40 references · Page 1 of 1

CITED BY

Showing 1-7 of 7 citing papers · Page 1 of 1